Edge-assisted adaptive offloading algorithm for 3D object detection tasks
Abstract
Multimodal 3D object detection is crucial for autonomous systems but suffers from high delay due to significant computational demands. To address this, we propose an edge computing-assisted framework that balances load between terminal devices and edge servers. We introduce dynamic threshold tuning and resolution-adaptive offloading algorithms to optimize performance. Experimental results demonstrate that our approach significantly reduces delay by minimizing offloading frequency while maintaining high accuracy, achieving a superior delay-accuracy trade-off. Furthermore, the framework exhibits robust adaptability across various models and bandwidth conditions, ensuring effectiveness in dynamic environments.
Fields of Study & Subjects
IoT and Edge/Fog Computing
Advanced Neural Network Applications
Robotics and Automated Systems
Publication details
- Journal / source
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Volume
- 21
- Issue
- 4
- Pages
- e0345876–e0345876
- Publication date
- 16 April 2026
- Language
- EN